fix: units and integs

Author: mufaddal-rohawala

src/sagemaker/utils.py

@@ -627,8 +627,8 @@ def retry_with_backoff(callable_func, num_attempts=8, botocore_client_error_code
             if not botocore_client_error_code or (
                 botocore_client_error_code
                 and isinstance(ex) == botocore.exceptions.ClientError
-                and ex.response["Error"]["Code"]
-                == botocore_client_error_code  # pylint: disable=no-member
+                and ex.response["Error"]["Code"]  # pylint: disable=no-member
+                == botocore_client_error_code
             ):
                 if i == num_attempts - 1:
                     raise ex

tests/integ/test_mxnet.py

@@ -24,7 +24,7 @@
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.mxnet.processing import MXNetProcessor
 from sagemaker.serverless import ServerlessInferenceConfig
-from sagemaker.utils import sagemaker_timestamp
+from sagemaker.utils import unique_name_from_base
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.kms_utils import get_or_create_kms_key
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
@@ -98,7 +98,7 @@ def test_framework_processing_job_with_deps(
 
 @pytest.mark.release
 def test_attach_deploy(mxnet_training_job, sagemaker_session, cpu_instance_type):
-    endpoint_name = "test-mxnet-attach-deploy-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-attach-deploy")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = MXNet.attach(mxnet_training_job, sagemaker_session=sagemaker_session)
@@ -165,7 +165,7 @@ def test_deploy_model(
     mxnet_inference_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -200,7 +200,7 @@ def test_register_model_package(
     mxnet_inference_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -216,7 +216,7 @@ def test_register_model_package(
             sagemaker_session=sagemaker_session,
             framework_version=mxnet_inference_latest_version,
         )
-        model_package_name = "register-model-package-{}".format(sagemaker_timestamp())
+        model_package_name = unique_name_from_base("register-model-package")
         model_pkg = model.register(
             content_types=["application/json"],
             response_types=["application/json"],
@@ -239,13 +239,13 @@ def test_register_model_package_versioned(
     mxnet_inference_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
             TrainingJobName=mxnet_training_job
         )
-        model_package_group_name = "register-model-package-{}".format(sagemaker_timestamp())
+        model_package_group_name = unique_name_from_base("register-model-package")
         sagemaker_session.sagemaker_client.create_model_package_group(
             ModelPackageGroupName=model_package_group_name
         )
@@ -287,7 +287,7 @@ def test_deploy_model_with_tags_and_kms(
     mxnet_inference_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -347,7 +347,7 @@ def test_deploy_model_and_update_endpoint(
     cpu_instance_type,
     alternative_cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -395,7 +395,7 @@ def test_deploy_model_with_accelerator(
     mxnet_eia_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-deploy-model-ei-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model-ei")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -426,7 +426,7 @@ def test_deploy_model_with_serverless_inference_config(
     mxnet_inference_latest_version,
     mxnet_inference_latest_py_version,
 ):
-    endpoint_name = "test-mxnet-deploy-model-serverless-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-deploy-model-serverless")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -465,7 +465,7 @@ def test_async_fit(
     mxnet_inference_latest_py_version,
     cpu_instance_type,
 ):
-    endpoint_name = "test-mxnet-attach-deploy-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-mxnet-attach-deploy")
 
     with timeout(minutes=5):
         script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist.py")

tests/integ/test_pytorch.py

@@ -20,7 +20,7 @@
 from sagemaker.pytorch.model import PyTorchModel
 from sagemaker.pytorch.processing import PyTorchProcessor
 from sagemaker.serverless import ServerlessInferenceConfig
-from sagemaker.utils import sagemaker_timestamp
+from sagemaker.utils import unique_name_from_base
 from tests.integ import (
     test_region,
     DATA_DIR,
@@ -130,7 +130,7 @@ def test_framework_processing_job_with_deps(
 def test_fit_deploy(
     pytorch_training_job_with_latest_infernce_version, sagemaker_session, cpu_instance_type
 ):
-    endpoint_name = "test-pytorch-sync-fit-attach-deploy{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-pytorch-sync-fit-attach-deploy")
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = PyTorch.attach(
             pytorch_training_job_with_latest_infernce_version, sagemaker_session=sagemaker_session
@@ -180,7 +180,7 @@ def test_deploy_model(
     pytorch_inference_latest_version,
     pytorch_inference_latest_py_version,
 ):
-    endpoint_name = "test-pytorch-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-pytorch-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -210,7 +210,7 @@ def test_deploy_packed_model_with_entry_point_name(
     pytorch_inference_latest_version,
     pytorch_inference_latest_py_version,
 ):
-    endpoint_name = "test-pytorch-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-pytorch-deploy-model")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model_data = sagemaker_session.upload_data(path=PACKED_MODEL)
@@ -240,7 +240,7 @@ def test_deploy_model_with_accelerator(
     pytorch_eia_latest_version,
     pytorch_eia_latest_py_version,
 ):
-    endpoint_name = "test-pytorch-deploy-eia-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-pytorch-deploy-eia")
     model_data = sagemaker_session.upload_data(path=EIA_MODEL)
     pytorch = PyTorchModel(
         model_data,
@@ -272,7 +272,7 @@ def test_deploy_model_with_serverless_inference_config(
     pytorch_inference_latest_version,
     pytorch_inference_latest_py_version,
 ):
-    endpoint_name = "test-pytorch-deploy-model-serverless-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-pytorch-deploy-model-serverless")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(

tests/integ/test_sklearn.py

@@ -20,7 +20,7 @@
 
 from sagemaker.serverless import ServerlessInferenceConfig
 from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnProcessor
-from sagemaker.utils import sagemaker_timestamp, unique_name_from_base
+from sagemaker.utils import unique_name_from_base
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
@@ -119,7 +119,7 @@ def test_training_with_network_isolation(
     "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968"
 )
 def test_attach_deploy(sklearn_training_job, sagemaker_session, cpu_instance_type):
-    endpoint_name = "test-sklearn-attach-deploy-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-sklearn-attach-deploy")
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = SKLearn.attach(sklearn_training_job, sagemaker_session=sagemaker_session)
@@ -138,7 +138,7 @@ def test_deploy_model(
     sklearn_latest_version,
     sklearn_latest_py_version,
 ):
-    endpoint_name = "test-sklearn-deploy-model-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-sklearn-deploy-model")
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
             TrainingJobName=sklearn_training_job
@@ -162,7 +162,7 @@ def test_deploy_model_with_serverless_inference_config(
     sklearn_latest_version,
     sklearn_latest_py_version,
 ):
-    endpoint_name = "test-sklearn-deploy-model-serverless-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-sklearn-deploy-model-serverless")
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
             TrainingJobName=sklearn_training_job
@@ -192,7 +192,7 @@ def test_async_fit(
     sklearn_latest_version,
     sklearn_latest_py_version,
 ):
-    endpoint_name = "test-sklearn-attach-deploy-{}".format(sagemaker_timestamp())
+    endpoint_name = unique_name_from_base("test-sklearn-attach-deploy")
 
     with timeout(minutes=5):
         training_job_name = _run_mnist_training_job(